Triple

T19611080
Position Surface form Disambiguated ID Type / Status
Subject Landline series E470729 entity
Predicate hasNotableWork P4 FINISHED
Object Landline Red
Landline Red is a prominent work from the Landline series, known for its bold use of color and minimalist, linear composition.
E1387048 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Landline Red | Statement: [Landline series, hasNotableWork, Landline Red]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Landline Red
Context triple: [Landline series, hasNotableWork, Landline Red]
  • A. Landline
    Landline is a 2017 American comedy-drama film set in 1990s New York City that follows a family dealing with infidelity and personal upheaval, starring Jenny Slate.
  • B. Coldline
    Coldline is a Google Cloud Storage class designed for low-cost, long-term storage of infrequently accessed data with higher retrieval latency and fees.
  • C. Redline
    Redline is a 2007 American action film centered on high-stakes illegal street racing, exotic cars, and underground gambling.
  • D. Dark Red Line
    Dark Red Line is a mass rapid transit route known for its dark red color designation within an MRT system.
  • E. Baby Bells
    The Baby Bells were regional telephone companies created from the 1984 breakup of AT&T’s Bell System, which took over local phone service in different parts of the United States.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Landline Red
Triple: [Landline series, hasNotableWork, Landline Red]
Generated description
Landline Red is a prominent work from the Landline series, known for its bold use of color and minimalist, linear composition.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Landline Red
Target entity description: Landline Red is a prominent work from the Landline series, known for its bold use of color and minimalist, linear composition.
  • A. Landline
    Landline is a 2017 American comedy-drama film set in 1990s New York City that follows a family dealing with infidelity and personal upheaval, starring Jenny Slate.
  • B. Coldline
    Coldline is a Google Cloud Storage class designed for low-cost, long-term storage of infrequently accessed data with higher retrieval latency and fees.
  • C. Redline
    Redline is a 2007 American action film centered on high-stakes illegal street racing, exotic cars, and underground gambling.
  • D. Dark Red Line
    Dark Red Line is a mass rapid transit route known for its dark red color designation within an MRT system.
  • E. Baby Bells
    The Baby Bells were regional telephone companies created from the 1984 breakup of AT&T’s Bell System, which took over local phone service in different parts of the United States.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d8e510fa248190b7afb274a1d4cf73 completed April 10, 2026, 11:54 a.m.
NER Named-entity recognition batch_69e640cb180c8190ba96ffb69c24e2e1 completed April 20, 2026, 3:05 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0771b2075481908c18e903421703cf completed May 15, 2026, 7:19 p.m.
NEDg Description generation batch_6a0772b3e6c88190b341479f838093f8 completed May 15, 2026, 7:23 p.m.
NED2 Entity disambiguation (via description) batch_6a077334652c8190b044d444e3004f29 completed May 15, 2026, 7:25 p.m.
Created at: April 10, 2026, 1:43 p.m.